1. Field of the Invention
The invention relates to apparatus and method for processing communications, suitable particularly but not exclusively for assisting in the management of information flows for a user.
2. Related Art
Modern computers are multifunctional devices that enable a user to process data not only from a local source but also from remote sources connected through local area networks and wide area networks such as the internet. The user can send and receive emails and generally use the computer as a workstation from which many different day-to-day tasks can be performed.
There are several journal publications that present various methods of filtering email messages, with the goal of reducing the cognitive load on the user. One such method has been described in “CAFE: a conceptual model for managing information in electronic mail”, XP000775829, where messages are routed into folders using a naïve Bayes classifier. The system filters the messages based on information in the ‘subject’, and ‘from’ fields and learns how to filter messages into various folders based on this information. Publication XP002099560, “Concept features in Re: Agent, an intelligent agent”, reviews various algorithms that are used for extracting information from the text of an email, which information is subsequently used to filter incoming emails based on the substance contained therein. Publication XP002099561, “Recurrent and feed-forward networks for human-computer interaction”, teaches the use of neural networks to extract information from a message, to learn how a user responds to that information, and to propose system actions upon receipt of subsequent emails. These actions may include displaying the message, saving the message, and initiating a reply window etc. Publication XP002081149, “Learning the rules that classify email”, compares various methods that may be used to learn rules for classifying emails, which can then be used to form message and mailbox categories. The classification includes classifying the sender, and the text within the message, and uses a Bayes net to represent a causal relationship between subject matter and the rules learnt therefrom. In all of these cases, the focus is on extracting text from the content and/or subject of emails, and using this text to classify the emails in some way.